Final Project (part 1)
Let’s prepara our data and plots. We should also save the date before the final plot.
# clean memory
rm(list = ls())
# link
link='eduwa.rda'
#getting the data TABLE from the file:
load(file=link)
eduwa=eduwa[complete.cases(eduwa),]#no missing values
# THE BOXPLOT
library(ggplot2)
## base
base= ggplot(eduwa,
aes(x=0,y = Reduced.Lunch)) +
theme_classic()
## boxplot
b1= base + geom_boxplot()
## horizontal
b1=b1 +coord_flip()
## no text on the vertical
b1=b1+ theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank())
## customizing titles
textForCaption="Source: WA State official records\nNote: Relevant values for boxplot shown on axis.\nMean value in blue."
BoxReducedLunch=b1 + labs(title = "Reduced lunch distribution",
subtitle = "All grades - WA State (2023)",
caption = textForCaption,
y= "Count of Students benefitted")
BoxReducedLunch
# clean memory
rm(list = ls())
#read in data frame
tableFreq=read.csv("tableFreq.csv")
#THE LOLLIPOP
## base
base = ggplot(tableFreq, aes(x=Locale,
y=gap)) +
theme_classic()
## order of sticks
base= base + scale_x_discrete(limits=tableFreq$Locale)
# making the sticks
lp1=base + geom_segment(aes(y = 0, yend = gap,
x = Locale,
xend = Locale),
color = "gray")
# making the candy
lp2=lp1 + geom_point(aes(color=PositiveGap))
# adding text
lp3= lp2 + geom_text(aes(label = round(gap,1)),
nudge_x=0.15,#move to the right
show.legend = FALSE)
# line at y=0
lp4 = lp3 + geom_hline(yintercept = 0)
# customizing axis: element_blank() means NOT TO SHOW
lp5 = lp4 + theme(axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
axis.text.y = element_blank())
lp6=lp5 + theme(axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
axis.text.x = element_blank())
# labels for sticks
lp7 = lp6 + geom_label(aes(label=Locale),
color ='black ',
size =3,
y=0,
show.legend = FALSE )
# customizing legend position
lp8= lp7 + theme(legend.position = c(0.8,0.3),
legend.background = element_rect(color='black'))
# labs
textTitle="Share of schools by Location"
textCapt="Source: WA Official Records.\nNote: Lines represent distance from 25%"
LolliSchoolsLocation=lp8 + labs(title = textTitle,
subtitle = "WA State 2023",
caption = textCapt,
color="Above 25%?")
LolliSchoolsLocation
rm(list = ls())
CrimeDayDF=read.csv("CrimebyDaytime_2019_22.csv")
# new base
base = ggplot(CrimeDayDF,
aes(x = reorder(crime, share),
y = share ) ) + theme_minimal()
#bars
bars = base + geom_bar(stat = "identity" )
#bars facetted and flipped
barsFct = bars + facet_grid( ~ daytime) + coord_flip()
# customizing y axis
barsFct=barsFct + theme(axis.text.y = element_text(size=7,
angle = 20))
# customizing text in bars
barsFct=barsFct + geom_text(aes(label=ifelse(share>9,
share,
"")),
nudge_y = 5,
size=2.5)
# labs
textCaption="Source: Seattle Open Data Portal\nNote: Annotations if >=10%"
barsCrimeDay=barsFct + labs(title = "Crimes by daytime",
subtitle = "Seattle - WA, 2019-2022",
x=" ", y= "Percent (%)",
caption = textCaption)
barsCrimeDay
# clear memory
rm(list = ls())
library(ggplot2)
crimeDate=readRDS("crimeWeeklyCount.rds")
base=ggplot(crimeDate,
aes(x=date,y=count))
lines=base + geom_line(alpha=0.3) + stat_smooth(color = "red",
method = "loess")
#labs
captionText="Source: Seattle Open Data Portal\nNote:Trend computed using loess algorithm"
Line_crimeTime=lines + labs(title = "Crimes in Seattle-WA",
subtitle = "Weekly count, 2019-2022",
caption = captionText)
Line_crimeTime
# clean memory
rm(list = ls())
safeINS_long=read.csv("safeINS_long.csv")
base = ggplot(data=safeINS_long,
aes(x=reorder(city, value,median),
y=value)) + theme_classic()
point=base+geom_point(shape=5)
lolli=point+geom_segment(aes(x=city,xend = city,
y=0,yend = value),
color='grey',linewidth=0.2)
lolliFacet=lolli+facet_grid(~variable) + coord_flip()
lolliSafetyCity= lolliFacet+ theme(axis.text.y = element_text(size = 5)) +
labs(title = "Safety in cities",
subtitle = "(Scores on Measures taken)",
caption = "Source: The Economist",
y="Percent(%)",
x="")
lolliSafetyCity
# clean memory
rm(list = ls())
allIN=read.csv('allIN.csv')
library(ggrepel)
base=ggplot(allIN,aes(x=dim1,y=dim2,
label = city)) + theme_void()
#labs
textCaption="Source:The Economist\nNote: Clustering process followed k-medoids technique."
textLegend="Clusters\n(labels do not\nrepresent order)"
ps=base + labs(title = "Safety of cities",
subtitle = "Scores on interventions (2023)",
caption=textCaption,
color=textLegend)
PointCitiesCluster=ps + geom_text(aes(color=as.factor(cluster)),
size=1.5)
pointsHere=ps + geom_point(aes(color=as.factor(cluster)))
pointsHere=pointsHere + geom_text_repel(size=1.5,
max.overlaps = 20)
pointsHere=pointsHere + theme(legend.background = element_rect(color='grey90'),
legend.title = element_text(size = 6,hjust = 0),
legend.position=c(0.2,0.05),
legend.direction = "horizontal")
pointsHere
# clean memory
rm(list = ls())
library(sf)
MapCount=read_sf("NeighborhoodCrime_polygons_Long.geojson")
library(ggplot2)
base=ggplot(data=MapCount) + theme_void()
mapYears=base + geom_sf(aes(fill=count),
color=NA)
mapYears=mapYears + facet_grid(~year)
mapYears=mapYears + scale_fill_gradient(low = 'yellow',
high= 'blue')
polygonMapCrime=mapYears + labs(title = "Crime events in Seattle by year",
subtitle = "All crime types (2019-2022)",
caption="Source: Seattle Open Data Portal\nand Seattle Police Department.")
polygonMapCrime
# clean memory
rm(list = ls())
library(sf)
PointLocations=read_sf("NeighborhoodCrime_points_worst4.geojson")
library(ggplot2)
base=ggplot(data=PointLocations) + theme_void()
aMap=base + geom_sf(aes(color=MCPP),size=1, alpha=0.2)
aMap=aMap+facet_grid(crimeMini~year)
#labs
textTitle="Crime events in Seattle by year and type"
textCapt="Source: Seattle Open Data Portal\nand Seattle Police Department."
subText="Showing the Worse 4 Neighborhoods (2019-2022)"
aMap=aMap + labs(title = textTitle,
subtitle = subText,
caption=textCapt)
# this WILL be exported separately
pointMapCrime=aMap + theme(strip.text.y = element_text(angle = -90))
#alpha for legend symbols
pointMapCrimeStatic= pointMapCrime +
guides(colour = guide_legend(override.aes =
list(alpha = 1)))
# reposition title and subtitle :
pointMapCrimeStatic= pointMapCrimeStatic +
theme(plot.title = element_text(vjust = 3,hjust=0.5),
plot.subtitle = element_text(vjust = 4,hjust = 0.5))
# reposition legend, and more
pointMapCrimeStatic= pointMapCrimeStatic + theme(
legend.position=c(-0.7,0.3),
#space between text and symbol in legend
legend.spacing.x = unit(0,units = 'cm'),
legend.text = element_text(size=6)) #text size
pointMapCrimeStatic
rm(list = ls())
library(leaflet)
library(sf)
NeighBrHdCount=read_sf("NeighborhoodCrime_polygons_Wide.geojson")
# preparePalette (no facet -then manually)
crimeLong=read.csv("crimeYearLong.csv")
paletteHere="YlOrBr"
Cuts=5
paletteFun=colorQuantile(palette = paletteHere,
domain = crimeLong$count,
n = Cuts)# N bins (quantiles)
LeafletPolyg2019 = leaflet() %>% addTiles() %>%
addPolygons(data=NeighBrHdCount,
label=~paste0(NEIGHBORHOOD,': ',count_2019),
weight = 0, #width border
opacity = 1, # alpha of border
fillOpacity = 0.8, # alpha fill
fillColor = ~paletteFun(count_2019)) %>%
addLegend(position='bottomleft',
pal=paletteFun,values = crimeLong$count)
LeafletPolyg2020 = leaflet() %>% addTiles() %>%
addPolygons(data=NeighBrHdCount,
label=~paste0(NEIGHBORHOOD,': ',count_2020),
weight = 0, #width border
opacity = 1, # alpha of border
fillOpacity = 0.8, # alpha fill
fillColor = ~paletteFun(count_2020)) # coloreando
LeafletPolyg2021 = leaflet() %>% addTiles() %>%
addPolygons(data=NeighBrHdCount,
label=~paste0(NEIGHBORHOOD,': ',count_2021),
weight = 0, #width border
opacity = 1, # alpha of border
fillOpacity = 0.8, # alpha fill
fillColor = ~paletteFun(count_2021))
LeafletPolyg2022 = leaflet() %>% addTiles() %>%
addPolygons(data=NeighBrHdCount,
label=~paste0(NEIGHBORHOOD,': ',count_2022),
weight = 0, #width border
opacity = 1, # alpha of border
fillOpacity = 0.8, # alpha fill
fillColor = ~paletteFun(count_2022)) %>%
addLegend(position='topright',
pal=paletteFun,values = crimeLong$count,
labFormat = function(type, cuts,p) {
n = length(cuts)
cuts = paste0(cuts[-n], " - ",
cuts[-1])
})
#####
library(manipulateWidget)
combineWidgets(LeafletPolyg2019,LeafletPolyg2020,
LeafletPolyg2021, LeafletPolyg2022)saveRDS(LeafletPolyg2019,"LeafletPolyg2019.rds")
saveRDS(LeafletPolyg2020,"LeafletPolyg2020.rds")
saveRDS(LeafletPolyg2021,"LeafletPolyg2021.rds")
saveRDS(LeafletPolyg2022,"LeafletPolyg2022.rds")rm(list = ls())
library(leaflet)
library(sf)
PointLocations=read_sf("NeighborhoodCrime_points_worst4.geojson")
library(htmltools)
title2019 <- tags$p(tags$b("2019"))
title2020 <- tags$p(tags$b("2020"))
title2021 <- tags$p(tags$b("2021"))
title2022 <- tags$p(tags$b("2022"))
MyCrimeGroup=c("Asault", "Burglary","Theft","Vandalism")
Points19=PointLocations[PointLocations$year==2019,]
Points20=PointLocations[PointLocations$year==2020,]
Points21=PointLocations[PointLocations$year==2021,]
Points22=PointLocations[PointLocations$year==2022,]
LeafletPoints2019=leaflet() %>% addTiles() %>%
addCircleMarkers(data=Points19[Points19$crimeMini=='Asault',],
radius=1,
group = "Asault")%>%
addCircleMarkers(data = Points19[Points19$crimeMini=='Burglary',],
radius=1,
group = "Burglary")%>%
addCircleMarkers(data = Points19[Points19$crimeMini=='Theft',],
radius=1,
group = "Theft")%>%
addCircleMarkers(data = Points19[Points19$crimeMini=='Vandalism',],
radius=1,
group = "Vandalism")%>%
addLayersControl(baseGroups = MyCrimeGroup,
options = layersControlOptions(collapsed = FALSE))%>%
addControl(title2019, position = "bottomright" )
LeafletPoints2020=leaflet() %>% addTiles() %>%
addCircleMarkers(data=Points20[Points20$crimeMini=='Asault',],
radius=1,
group = "Asault")%>%
addCircleMarkers(data = Points20[Points20$crimeMini=='Burglary',],
radius=1,
group = "Burglary")%>%
addCircleMarkers(data = Points20[Points20$crimeMini=='Theft',],
radius=1,
group = "Theft")%>%
addCircleMarkers(data = Points20[Points20$crimeMini=='Vandalism',],
radius=1,group = "Vandalism")%>%
addLayersControl(baseGroups = MyCrimeGroup,
options = layersControlOptions(collapsed = FALSE))%>%
addControl(title2020, position = "bottomright" )
##
LeafletPoints2021=leaflet() %>% addTiles() %>%
addCircleMarkers(data=Points21[Points21$crimeMini=='Asault',],
radius=1,
group = "Asault")%>%
addCircleMarkers(data = Points21[Points21$crimeMini=='Burglary',],
radius=1,
group = "Burglary")%>%
addCircleMarkers(data = Points21[Points21$crimeMini=='Theft',],
radius=1,
group = "Theft")%>%
addCircleMarkers(data = Points21[Points21$crimeMini=='Vandalism',],
radius=1,
group = "Vandalism")%>%
addLayersControl(baseGroups = MyCrimeGroup,
options = layersControlOptions(collapsed = FALSE))%>%
addControl(title2021, position = "bottomright" )
###
LeafletPoints2022=leaflet() %>% addTiles() %>%
addCircleMarkers(data=Points22[Points22$crimeMini=='Asault',],
radius=1,
group = "Asault")%>%
addCircleMarkers(data = Points22[Points22$crimeMini=='Burglary',],
radius=1,
group = "Burglary")%>%
addCircleMarkers(data = Points22[Points22$crimeMini=='Theft',],
radius=1,
group = "Theft")%>%
addCircleMarkers(data = Points22[Points22$crimeMini=='Vandalism',],
radius=1,
group = "Vandalism")%>%
addLayersControl(baseGroups = MyCrimeGroup,
options = layersControlOptions(collapsed = FALSE))%>%
addControl(title2022, position = "bottomright" )
library(leaflet.minicharts) # syncWith
library(manipulateWidget) # combineWidgets
combineWidgets(LeafletPoints2019%>%
syncWith("maps"),
LeafletPoints2020%>%
syncWith("maps"),
LeafletPoints2021%>%
syncWith("maps"),
LeafletPoints2022%>%
syncWith("maps"))saveRDS(LeafletPoints2019,"LeafletPoints2019.rds")
saveRDS(LeafletPoints2020,"LeafletPoints2020.rds")
saveRDS(LeafletPoints2021,"LeafletPoints2021.rds")
saveRDS(LeafletPoints2022,"LeafletPoints2022.rds")LeafletPoints2019_clus=
leaflet() %>% addTiles() %>%
addMarkers(data=Points19[Points19$crimeMini=='Asault',],
group = "Asault",
clusterOptions = markerClusterOptions())%>%
addMarkers(data=Points19[Points19$crimeMini=='Burglary',],
group = "Burglary",
clusterOptions = markerClusterOptions())%>%
addMarkers(data = Points19[Points19$crimeMini=='Theft',],
group = "Theft",
clusterOptions = markerClusterOptions())%>%
addMarkers(data = Points19[Points19$crimeMini=='Vandalism',],
group = "Vandalism",
clusterOptions = markerClusterOptions())%>%
addLayersControl(overlayGroups = MyCrimeGroup,
options = layersControlOptions(collapsed = FALSE))%>%
addControl(title2019, position = "bottomright" )
LeafletPoints2019_clussaveRDS(LeafletPoints2019_clus,"LeafletPoints2019_clus.rds")